The Kobresia pygmaea alpine meadow is a main vegetation type in the Qinghai-Tibetan Plateau. An accurate detection of the green-up dates for K. pygmaea is important to simulate and predict vegetation phenology shifts under the influence of c limate change in the Qinghai-Tibetan Plateau. Green-up date estimation methods from remote sensing data generally include two processes: reconstruction of high-quality vegetation index time-series data through noise removal and calculation of green-up dates from the reconstructed vegetation index time series. The reconstruction methods for vegetation index time-series d ata can be divided into two categories: filter fitting and curve fitting methods. The green-up date retrieval methods include the threshold

maximum slope

curvature

and moving average methods. The green-up date identification method is a combination of the reconstruction methods for vegetation index time-series data and the retrieval methods for green-up dates under different study conditions. The a ccuracy of the green-up date identification methods is usually affected by many factors

such as specific geographic location

prior experience

parameterization

and initial parameters. In this study

we adopted a simulated annealing algorithm to optimize the r econstruction process and thus avoid the problems of low efficiency and local optimum caused by traditional optimal methods. We first used the double-Gaussian

double-Logistic

and polynomial functions to reconstruct the Normalized Difference Vegetation I ndex（ NDVI）time series. After evaluations with visual inspections and root mean square error

we identified the most feasible r econstruction method. We then used the maximum slope

threshold

curvature

and dynamic threshold methods to derive the green-up dates from the best reconstructed NDVI time series. The performance of these three methods for green-up date identification were tested using the green-up data from 34 ground observation samples and their corresponding National Oceanic and A tmospheric Administration NDVI time-series data at 8-kilometer resolution. We selected additional 153 samples

which were e venly distributed in the K. pygmaea alpine meadow in the Qinghai-Tibetan Plateau

to test the identified optimal green-up estimation method and to investigate the changes in green-up dates in the study area. The reconstructed NDVI time series with the double-Gaussian function had the smallest deviation from the original NDVI time series

and the noises can be reduced effectively through the double-Gaussian fitting process.Therefore

the aforementioned method was the most suitable for describing the intra-annual growth cycle of the K. pygmaea alpine meadow. The reconstructed NDVI time series with the double-Gaussian function method indicated that the green-up dates identified with the maximum slope threshold method agreed with the observed ground phenology data. The correlation coefficients between the identified green-up dates and the observed dates were 0. 823（ P < 0. 001） and 0. 646（ P < 0. 01） at the Haiyan and Gande stations

respectively. The average green-up dates for the K. pygmaea alpine meadow in the Qinghai-Tibetan Plateau were mainly located between DOY（ Day of Year） 120（ i. e.

30 April） and 140（ i. e.

20 May）. The green-up onset date advanced by an average of 7 days from 1982 to 2011 in the study region.